Estimating confidence scores for recognition results is a classic task in ASR field and of vital importance for kinds of downstream tasks and training strategies. Previous end-to-end~(E2E) based confidence estimation models (CEM) predict score sequences of equal length with input transcriptions, leading to unreliable estimation when deletion and insertion errors occur. In this paper we proposed CIF-Aligned confidence estimation model (CA-CEM) to achieve accurate and reliable confidence estimation based on novel non-autoregressive E2E ASR model - Paraformer. CA-CEM utilizes the modeling character of continuous integrate-and-fire (CIF) mechanism to generate token-synchronous acoustic embedding, which solves the estimation failure issue above. We measure the quality of estimation with AUC and RMSE in token level and ECE-U - a proposed metrics in utterance level. CA-CEM gains 24% and 19% relative reduction on ECE-U and also better AUC and RMSE on two test sets. Furthermore, we conduct analysis to explore the potential of CEM for different ASR related usage.

翻译：置信度分数估计是自动语音识别（ASR）领域的一项经典任务，对各类下游任务和训练策略至关重要。以往的端到端置信度估计模型（CEM）预测与输入转录等长的分数序列，在出现删除和插入错误时会导致不可靠的估计。本文基于新型非自回归端到端ASR模型Paraformer，提出CIF对齐置信度估计模型（CA-CEM），以实现准确且可靠的置信度估计。CA-CEM利用连续积分触发（CIF）机制的建模特性生成词级同步声学嵌入，从而解决了上述估计失效问题。我们采用词级AUC和RMSE及提出的语句级指标ECE-U评估估计质量。CA-CEM在两个测试集上的ECE-U分别降低24%和19%，同时AUC和RMSE指标更优。此外，我们通过分析探索了CEM在ASR相关不同应用场景中的潜力。